Akshay U. Bhat

Incidence and implications of postoperative supraventricular tachycardia after pulmonary lobectomy

OBJECTIVE We sought to determine the rate of postoperative supraventricular tachycardia (POSVT) in patients undergoing pulmonary lobectomy, and its association with adverse outcomes. METHODS Using the State Inpatient Database, from the Healthcare Cost and Utilization Project, we reviewed lobectomies performed (2009-2011) in California, Florida, and New York, to determine POSVT incidence. Patients were grouped by presence or absence of POSVT, with or without other complications. Stroke rates were analyzed independently from other complications. Multivariable regression analysis was used to determine factors associated with POSVT. RESULTS Among 20,695 lobectomies performed, 2449 (11.8%) patients had POSVT, including 1116 (5.4%) with isolated POSVT and 1333 (6.4%) with POSVT with other complications. Clinical predictors of POSVT included age ≥75 years, male gender, white race, chronic obstructive pulmonary disease, congestive heart failure, thoracotomy surgical approach, and pulmonary complications. POSVT was associated with an increase of: stroke (odds ratio [OR] 1.74; 95% confidence interval [CI] 1.03-2.94); in-hospital death (OR 1.85; 95% CI 1.45-2.35); LOS (OR 1.33; 95% CI 1.29-1.37); and readmission (OR 1.29; 95% CI 1.04-1.60). The stroke rate was <1% in patients who had isolated POSVT, and 1.5% in patients with POSVT with other complications. Patients with isolated POSVT had increased readmission and LOS, and a marginal increase in stroke rate, compared with patients with an uncomplicated course. CONCLUSIONS POSVT is common in patients undergoing pulmonary lobectomy and is associated with adverse outcomes. Comparative studies are needed to determine whether strict adherence to recently published guidelines will decrease the rate of stroke, readmission, and death after POSVT in thoracic surgical patients.

Variability in length of stay after uncomplicated pulmonary lobectomy: is length of stay a quality metric or a patient metric?†.

OBJECTIVES Previous studies have identified predictors of prolonged length of stay (LOS) following pulmonary lobectomy. LOS is typically described to have a direct relationship to postoperative complications. We sought to determine the LOS and factors associated with variability after uncomplicated pulmonary lobectomy. METHODS Analysing the State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality database, we reviewed lobectomies performed (2009-11) on patients in California, Florida and New York. LOS and comorbidities were identified. Multivariable regression analysis (MVA) was used to determine factors associated with LOS greater than the median. Patients with postoperative complications or death were excluded. RESULTS Among 22 647 lobectomies performed, we identified 13 099 patients (58%) with uncomplicated postoperative courses (mean age = 66 years; 56% female; 76% white, 57% Medicare; median DEYO comorbidity score = 3, 55% thoracotomy, 45% thoracoscopy/robotic). There was a wide distribution in LOS [median LOS = 5 days; interquartile range (IQR) 4-7]. By MVA, predictors of prolonged LOS included, age ≥ 75 years [odds ratio (OR) 1.7, 95% confidence interval (CI) 1.4-2.0], male gender (OR 1.2, 95% CI 1.1-1.2), chronic obstructive pulmonary disease (OR 1.6, 95% CI 1.5-1.7) and other comorbidities, Medicaid payer (OR 1.7, 95% CI 1.4-2.1) versus private insurance, thoracotomy (OR 3.0, 95% CI 2.8-3.3) versus video-assisted thoracoscopic surgery/robotic approach and low hospital volume (OR 2.4, 95% CI 2.1-2.6). CONCLUSIONS Variability exists in LOS following even uncomplicated pulmonary lobectomy. Variability is driven by clinical factors such as age, gender, payer and comorbidities, but also by surgical approach and volume. All of these factors should be taken into account when designing clinical care pathways or when allocating payment resources. Attempts to define an optimal LOS depend heavily upon the patient population studied.

Automatic selection of radiological protocols using machine learning

Medical imaging modalities, such as computed tomography (CT), have a large number of parameters that must be correctly set to produce a diagnostic image. In current clinical practice this is done with input from a radiologist, relying on the patient history provided in textual form by the referring physician. Since the set of parameters is so extensive, radiologists choose from a limited number of protocols, each of which is suited to a group of diseases. We propose a machine learning approach automate to this process, relying on the free-form textual input provided by the referring physician. We exploit an ontology built by the National Library of Medicine to provide domain expertise, as well as an associated parser that maps free-form text into this ontology. We use a graph-based semi-supervised learning technique, where the nodes of the graph are concepts from the ontology and the labels are protocols. The semi-supervised learning approach is motivated by the easy availability of unlabeled training data, for example from patient histories where the protocol is unknown, or even patient histories who did not receive imaging. In our initial experiments we used the adsorption algorithm of [2], running on a database of 1,000 patients who were assigned a protocol by radiologists at New York Presbyterian Hospital. On a stratified sample from our dataset we predicted the protocol selected by a radiologist 60% of the time, compared with a baseline accuracy of 20% achieved by always predicting the most popular protocol. Our results suggest that modern machine learning and NLP techniques show considerable promise for solving this important clinical problem.